18F-FDG PET/CT Radiomics for Predicting Therapy Response in Primary Mediastinal B-Cell Lymphoma: A Bi-Centric Pilot Study.
Simple Summary: This bi-centric pilot study investigates the predictive value of pre-treatment [18F]FDG PET/CT radiomics for therapy response in primary mediastinal B-cell lymphoma (PMBCL). Patients from two Italian centers underwent baseline PET/CT, and the Deauville score (DS) was used to define r...
| Publicado en: | Cancers Vol. 17; no. 11; pp. 1827 - 1842 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
MDPI
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185869488&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185869488 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jun2025 vid: 17 iid: 11 pid: 97109 pub: MDPI artinfo: ui: 185869488 185869488 185869488 10.3390/cancers17111827 185869488 ppf: 1827 ppct: 15 formats: tig: atl: 18F-FDG PET/CT Radiomics for Predicting Therapy Response in Primary Mediastinal B-Cell Lymphoma: A Bi-Centric Pilot Study. aug: au: Esposito, Fabiana Manco, Luigi Urso, Luca Adamantiadis, Sara Scribano, Giovanni De Marchi, Lucrezia Venditti, Adriano Postorino, Massimiliano Urbano, Nicoletta Gafà, Roberta Cuneo, Antonio Chiaravalloti, Agostino Bartolomei, Mirco Filippi, Luca affil: Hematology, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy sug: subj: Mediastinal Neoplasms Radiography Mediastinal Neoplasms Diagnosis Mediastinal Neoplasms Therapy Lymphoma, B-Cell Radiography Lymphoma, B-Cell Diagnosis Lymphoma, B-Cell Therapy Tomography, Emission-Computed Methods Tomography, X-Ray Computed Methods Fludeoxyglucose F 18 Radiomics Methods Predictive Value of Tests Human Male Female Adult Retrospective Design Record Review Pilot Studies Artificial Intelligence Random Forest Support Vector Machine Machine Learning Algorithms ROC Curve Sensitivity and Specificity Descriptive Statistics Adult: 19-44 years Male Female ab: Simple Summary: This bi-centric pilot study investigates the predictive value of pre-treatment [18F]FDG PET/CT radiomics for therapy response in primary mediastinal B-cell lymphoma (PMBCL). Patients from two Italian centers underwent baseline PET/CT, and the Deauville score (DS) was used to define response (DS1–3 vs. DS4–5). Radiomic features (RFts) were extracted from manually segmented PET and CT images and harmonized across centers. Two machine learning models (PET and CT) were trained with Random Forest and Support Vector Machine algorithms. In external validation, the best-performing SVM classifier showed AUCs of 0.80 (PET) and 0.75 (CT), with accuracies of 77% and 85%, respectively. Both models demonstrated strong specificity and precision. These results suggest that PET/CT radiomics-based ML models may assist in predicting treatment response in PMBCL patients. Purpose: This bi-centric pilot study investigates the predictive value of pre-treatment [18F]FDG PET/CT radiomics for assessing therapy response in primary mediastinal B-cell lymphoma (PMBCL). Methods: All PMBCL patients underwent PET/CT with [18F]FDG between January 2011 and January 2022 at Policlinico Tor Vergata University Hospital of Rome (70% training and 30% internal validation cohort) and Sant'Anna University Hospital of Ferrara (external validation cohort). The Deauville score (DS) was used as a predictor of therapy response (DS1-DS3 vs. DS4/DS5). A total of 121 quantitative radiomics features (RFts) were extracted from manually segmented volumes of interest (VOIs) in PET and CT images, according to IBSI. ComBat harmonization was applied to correct the center variability of features, followed by class balancing with SMOTE. Two machine learning (ML) prediction models, the PET model and the CT model, were independently developed using robust RFts. For each ML model, two different algorithms were trained (i.e., Random Forest, RF, and Support Vector Machine, SVM) using 10-fold cross validation, tested on the internal/external validation set. Receiver operating characteristic (ROC) curves, area under the curve (AUC), classification accuracy (CA), precision (Prec), sensitivity (Sen), specificity (Spec), true positive (TP) scores, and true negative (TN) scores were computed. Results: The entire dataset was composed of 29 samples for the Rome cohort (23 from D1–D3 and 6 from D4/D5) and 9 samples for the Ferrara cohort (4 from D1–D3 and 5 from D4/D5). A total of 27 RFts were identified as robust for each imaging modality. Both the CT and PET models effectively predicted the Deauville score. The performance metrics of the best classifier (SVM) for the CT and PET models in external validation were AUC = 0.75/0.80, CA = 0.85/0.77, Prec = 0.97/0.67, Sen = 0.60/0.80, Spec = 0.98/0.75, TP = 75.0%/66.7%, and TN = 77.8%/85.7%, respectively. Conclusions: ML models trained on [18F]FDG PET/CT radiomic features in PMBLC patients could predict the Deauville score. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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